[WSS18] Hearty- A Microsite That Helps You Reflect on Your Day

This program uses machine learning with the Classify function in the Wolfram Language to analyze the sentiment of text input and face expressions to identify actions and sources of positive, negative, and neutral influences on your mood. It outputs word clouds that show the user how much certain activities or actions may influence their mood. It summarizes how negative and positive the person's day may have been based on the sentiment detected from each sentence of their daily log. It also detects how the person may have felt in a given day based on the facial expression present in the profile picture they put in.

Finally, here is the section of the code with all of the functions defined above that analyzes the input and deploys the program to the cloud. It uses WordCloud on each of the groups of nouns and verbs sorted by sentiment to look at which words are used the most in the inputted daily log. Additionally, it determines how the user is feeling based on the facial expression in their profile picture.

Here is an example of what happens when you enter in a paragraph describing your day and a picture:

Future Work

Machine learning can be further applied to a series of daily logs and selfies to look for patterns of mood and activities that the user inputs as well as identify stress factors. If implemented in a smartphone application, the program can be made to send notifications to the user when the algorithm determines it would be beneficial to the user's wellbeing. With the use of neural networks and other sources of data such as from Apple Health, more categories can be added to identify more specific moods such as anger, excitement, and sadness.